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December 8, 2025BloodOpen Access

Machine learning based survival prediction of DLBCL: Using multimodal data.

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Authors

RKRashmi KhanalSNShazia NakhodaMWMariusz Wasik

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Overview

Machine learning shows improved survival prediction in DLBCL using clinical and gene expression data, suggesting enhanced outcomes through advanced modeling techniques.

Key Points

  • Overall survival was effectively predicted using multimodal data including gene expression and clinical factors.
  • Machine learning models demonstrated varying accuracies, with RandomForest achieving up to 87.8% accuracy for 6-month survival.
  • Analysis utilized multiple machine learning algorithms to assess survival outcomes in 1,311 DLBCL patients.
  • Findings highlight the need for external validation of predictive models to ensure robustness in clinical settings.

Cite This Study

Khanal et al. (2025) studied this question.

synapsesocial.com/papers/69362f444fa91c937236d573https://doi.org/10.1182/blood-2025-7068
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Development of a machine learning model to predict overall survival in patients with peripheral T-cell lymphoma in a minority enriched population2025
  2. 2Real-world patient-based next-generation sequencing assessments identify a high-risk subgroup and associated gene signature in diffuse large B cell lymphoma2025
  3. 3Uncovering survival-linked genes in DLBCL: A transcriptomic analysis of 1,311 patients2025
  4. 4Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis2025 · 1 citations
  5. 5Early detection of advanced-stage DLBCL using random forest: Uncovering large-scale demographic disparities and site-specific risk patterns2025